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Dual-Mode Forward Collision Avoidance Algorithm Based on Vehicle-to-Vehicle (V2V) Communication
Conference Paper · August 2018
DOI: 10.1109/MWSCAS.2018.8623896
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Ahmed Hosny
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Abstract—This paper proposes a Vehicle-to-Vehicle (V2V) communication-based forward collision avoidance algorithm by alarming the driver for the normal driver mode and controlling the driving wheel for the self-governed (autonomous) driving mode. The proposed algorithm benefits from the information exchange between the host vehicle and the leading vehicle to calculate the safe distance between host vehicle and leading vehicle to guarantee the avoidance of the collision. The proposed system gives advisory and imminent warnings according to the predicted accident levels, using a three different levels of collision avoidance for the driver mode. Also, in the autonomous driving mode, it follows an alternative optimal path to avoid the collision. The simulation results are implemented using Prescan and MATLAB.
The simulation results show that the proposed collision avoidance system makes a composite analysis of the collision risk and provides an accurate real-time warning and an alternative path for dual driving modes.
Keywords—Collision Avoidance, Vehicle-to-Vehicle (V2V), Prescan, Autonomous.
I. INTRODUCTION
Automobile industry occupies a pivotal position in technological research around the world, Billions of dollars are spent yearly on the field of research and development of cars capacities either in terms of luxury or safety. This opens the way for many of global corporations to invest Billions of dollars every year into research for the huge financial revenue and technological gains in favor of the corporations and economies around the world.
Safety element in cars is a basic pillar in the reduction of accidents around the world. According to the World Health Organization (WHO) 2016 statistics, 1.25 Million deaths worldwide were caused by car accidents [1], meaning that car accidents are the ninth leading cause of death around the globe, and the number is expected to rise to 1.9 Million by 2030, meaning that car accidents will be the fifth leading cause of death around the globe by 2030 [2]. This paper main objective is to reduce accidents resulting from driver error or road problems by avoiding the forward collision.
This work develops a forward collision avoidance algorithm that controls the vehicle to change or keep the presented lane to avoid a forward collision that may be caused by another vehicle.
The algorithm is based on the optimization problem provided by the same authors in [3,4] in a symbolic numerical and well- defined mathematical model. Some parameters are treated as a given input from an integrated Vehicle-to-Vehicle (V2V) or
Vehicle-to- Any device (V2X) systems, such as road’s obstacle dimensions and information on road’s velocity [4]. This work provides practical simulations for the mathematical model in [3,4] in collision avoidance by using Prescan tool and MATLAB.
The paper is organized as follows: Section II gives descriptions of the proposed forward collision algorithm. Simulation results are given in Section III. Finally, the whole work is concluded in Section IV.
II. PROPOSED FORWARD COLLISION ALGORITHM In this paper, the system model is based on three scenarios including the duality between the autonomous mode system and the warning system for the driver mode. The basic scenario contains two vehicles which are Leading Vehicle (LV) and Following Vehicle (FV). Every vehicle is provided by Global- Positioning-System (GPS) device and Radio Frequency (RF) [5]. Antenna System is used to transfer positioning data between LV and FV and is used in couple of autonomous and warning system that uses Basic Safety Message (BSM).
According to the Society of Automotive Engineers (SEA J2735) protocol [6], the BSM is the primary message set that contains the basic information to secure the driver life and to send data among vehicles.
The Following vehicle updates the safe braking distances as a function of the speed, latitude, and longitude (position values).
Using this information combined with the data received from the BSM sent by Leading Vehicle, the host vehicle calculates the Distance-To-Collision based on the acceleration (DTCa) between the two vehicles.
DTC is frequently used in literature as a descriptor of how urgent a situation has become, as well as potentially how a driver perceives stimuli during an event [7]. DTC is determined using various measures and theories. In an event that includes an FV and LV and when the LV is moving at a constant rate (zero acceleration), the DTC is computed based on the velocity only [8] as follows:
𝐷𝑇𝐶 = −𝑟
𝑉𝑟𝑉𝑓𝑣 (1)
Dual-Mode Forward Collision Avoidance Algorithm Based on Vehicle-to-Vehicle (V2V) Communication
Mohamed Yousef1, Ahmed Hosny1, Wessam Gamil1, Mohamed Adel1,2, Hazem M. Fahmy3, M. Saeed Darweesh1,4, Hassan Mostafa2,4
1Institute of Aviation Engineering and Technology, Giza, Egypt
2Faculty of Engineering, Cairo University, Giza, Egypt
3Germany University in Cairo, Cairo, Egypt
4Nanotechnology Department, Zewail City of Science and Technology, Egypt
978-1-5386-7392-8/18/$31.00 ©2018 IEEE 739
Where r is the range between the vehicles based on the Haversine formula [7], and 𝑉𝑟 is the relative velocity, which is defined as:
𝑉𝑟 = 𝑉𝑙𝑣− 𝑉𝑓𝑣 (2)
Where 𝑉𝑙𝑣 and 𝑉𝑓𝑣 are the speeds of the LV and the FV, respectively.
This paper proposes a dual mode forward collision avoidance system (driver mode and autonomous mode) based on the acceleration to be more practical and realistic model by calculating the DTCa when the FV acceleration is assumed to be zero and when the LV is deaccelerating. The LV acceleration is included in the equation of DTCa as follows:
𝐷𝑇𝐶𝑎 = −𝑉𝑟−√𝑉𝑟
2+2𝑎𝑙𝑣𝑟
𝑎𝑙𝑣 𝑉𝑓𝑣 (3) Where 𝑎𝑙𝑣 and −𝑉𝑟−√𝑉𝑟
2+2𝑎𝑙𝑣𝑟
𝑎𝑙𝑣 are the acceleration of the LV, and the time-to-collision, respectively.
In this paper there are three levels of thresholds defined for the warning distances considering the speed and acceleration of both vehicles (LV and FV) and the distance between them to calculate DTCa from (3). Warnings are triggered when the distance between the two vehicles are less than these defined thresholds.
The First threshold warning distance 𝐷𝑤1 is calculated from (4):
𝐷𝑤1= 0.5( 𝑉𝑓𝑣2
𝑎𝑓𝑣 −𝑉𝑙𝑣2
𝑎𝑙𝑣) + 𝑡𝑑𝑉𝑓𝑣+ 𝐷𝑜 (4)
Where 𝑎𝑓𝑣 and 𝑎𝑙𝑣 are the accelerations of the LV and the FV, respectively. The additional term 𝐷𝑜 is the distance between the host and the leading vehicle when they stop, it is used to inform the driver that there is a vehicle on the same lane.
The second threshold warning distance 𝐷𝑤2 calculated from (5) and it warns the driver to take a decision to change the lane (autonomous) or to decelerate the vehicle (driver mode).
𝐷𝑤2= 𝑉𝑓𝑣2
19.6(𝑎𝑓𝑣𝑔 +𝑓+𝐺)+ 𝑡𝑑𝑉𝑓𝑣+ 𝐷𝑜 (5) Equation (5) is commonly used in road design for establishing the minimum stopping sight distance required on a given road according to the American Association of State Highway and Transportation Officials (AASHTO) which gives the formula for calculating the stopping distance [9]. If the actual vehicle spacing drops below this threshold, then the distance-to- collision is less than the total distance travelled by the vehicle during the delay time (td) then the third threshold warning distance will be activated.
According to the second equation of motion [10], this work proposed forward collision avoidance system by proposing the
equation of the third threshold warning range in an LV situation after adding the acceleration as:
𝐷𝑤3= 𝑉𝑓𝑣𝑡𝑑− 0.5𝑎𝑙𝑣𝑡𝑑2+ 𝐷𝑜 (6) The third threshold warning range warns the driver urgently to take a decision to avoid the collision immediately. In case of no-response from the driver, the autonomous mode makes the car brake or change the lane if there is no coming car in the opposite lane.
At every incoming BSM, the algorithm checks the difference between the DTCa and the warning distances (𝐷𝑤1, 𝐷𝑤2, and 𝐷𝑤3) then calculates the time interval to reach the warning distance considering the current speed. If the obtained time to warning (𝑡𝑤) is smaller than the GPS update period (𝑡𝐺𝑃𝑆), a collision warning is triggered after 𝑡𝑤 seconds.
Algorithm 1 shows the proposed forward collision algorithm.
Algorithm 1: Proposed Forward Collision Algorithm Input: GPS update period(𝑡𝐺𝑃𝑆), Distance to collision
regrading acceleration (𝐷𝑇𝐶𝑎), Safe and
automatic braking distances
(𝐷𝑤1, 𝐷𝑤2, 𝐷𝑤3) and Host vehicle current speed (𝑉𝑓𝑣).
Output: Time to warning (𝑡𝑤1, 𝑡𝑤2, 𝑜𝑟 𝑡𝑤3) begin
∆𝐷1← DTC − 𝐷𝑤1
∆𝐷2← DTC − 𝐷𝑤2
∆𝐷3← DTC − 𝐷𝑤3 𝑡𝑤1← ∆𝐷1⁄V𝑓𝑣 𝑡𝑤2← ∆𝐷2⁄V𝑓𝑣 𝑡𝑤3← ∆𝐷3⁄V𝑓𝑣 if 𝑡𝑤3≤ 𝑡𝐺𝑃𝑆 then
return (𝑡𝑤3) else if 𝑡𝑤2≤ 𝑡𝐺𝑃𝑆 then
return (𝑡𝑤2) else if 𝑡𝑤1≤ 𝑡𝐺𝑃𝑆 then
return (𝑡𝑤1) end
end
The proposed optimization and control algorithm is shown in Fig. 1 where 𝑡𝑠𝑡𝑎𝑟𝑡 and 𝑡𝑒𝑛𝑑 are the limits of the whole simulation interval, 𝑡𝑠 and 𝑡𝑒 are the limits of each optimization iteration based on the discrete sampling. 𝐷𝑒𝑙𝑡𝑎𝑛𝑒𝑤 and 𝐵𝑒𝑡𝑎𝑛𝑒𝑤 are the new optimized steering angle and braking ratio for the current iteration that corresponds to a minimum 𝐶 which is the cost function. 𝐷𝑖𝑣 denotes the number of divisions that the whole interval should be sampled to. 𝑅𝑙𝑖𝑛𝑒 denotes the ideal line that the optimization should refer to when calculating the minimum cost function. 𝑉𝑚𝑎𝑥 and 𝑃𝑤𝑔 denotes the maximum allowed velocity and constraints violation penalty weights respectively [4]. Full details of the optimization problem is available in [3,4] by the same authors.
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Fig. 1: Optimization algorithm for collecting the data of each iteration and interpolate it into a vector that can be visualized after the process is done
III. SIMULATION RESULTS
In this work, the Prescan simulation tool connected to MATLAB to verify the performance of the proposed forward collision avoidance algorithm. The traffic scenario is implemented in the Prescan as shown in Fig. 2.
Fig. 2: Traffic Scenario in Prescan
The performance metric in this work is the success of generating a warning message for the driver then enter the suitable mode (driver mode or autonomous mode) to decelerate or brake or change the lane before the collision occurs.
The proposed forward collision avoidance algorithm is tested in different scenarios (driver and autonomous) as follows:
A. Driver Mode Scenario
• The LV deaccelerate, the driver of the FV is informed by the three levels of warnings before it takes the action of braking to stop before the collision occurs as shown in Figs. 3 and 4.
In case of no-response from the driver, the autonomous mode makes the car brake or change the lane if there is no coming car in the opposite lane.
Fig. 3: The first threshold warning
Fig. 4: The second threshold warning
• When the LV stops, the FV brakes before the collision occurs as shown in Fig. 5.
Fig. 5: The third threshold warning
B. Autonomous Mode Scenario
• The LV is moving slowly, while the FV is moving in higher speed and accordingly, the FV should pass the LV by changing the current lane then back to the original lane. In doing this lane change action, the algorithm ensures that there is no coming vehicle in the other lane to avoid the collision as shown in Figs. 6 and 7.
Fig. 6: The Following Vehicle acts to skip the Leading Vehicle
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Fig. 7: The Following Vehicle after skipping the Leading Vehicle
• The same previous scenario is repeated in case there is another vehicle coming in the other lane. Thus, the FV should deaccelerate and never change the lane to avoid the collision with the coming vehicle in the other lane as shown in Figs. 8 and 9.
Fig. 8: The autonomous mode with coming vehicle in another lane
Fig. 9: The Following Vehicle decelerate and keep its lane
• The same previous scenario and still there is another vehicle coming in the other lane, but in this scenario the FV wants to skip the LV. Therefore, the FV should wait the other lane vehicle to pass first and then take the action of skipping the LV by changing the lane as shown in Figs 10, 11 and 12.
Fig. 10: The autonomous mode with coming vehicle in another lane
Fig. 11: The Following Vehicle waits the other lane vehicle to pass then changes the lane
Fig. 12: The Following Vehicle after skipping the Leading Vehicle
IV. CONCLUSION
In this work, a proposed forward collision avoidance algorithm based on the vehicle acceleration which is used for warning the vehicle driver by three warning levels. Also, support autonomous mode for lane change and skip the Leading Vehicle to avoid the collision in all scenarios. The proposed algorithm has been tested for different scenarios to ensure the ability to avoid the collision in all these scenarios using the Prescan simulation environment and MATLAB.
ACKNOWLEDGMENT
This work was performed on a project at Institute of Aviation Engineering and Technology (IAET) funded by the Academy of Scientific Research and Technology (ASRT). This research was partially funded by ONE Lab at Cairo University, Zewail City of Science and Technology, and KAUST.
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